Stability Analysis for Neural Networks with Time-Varying Delay

Ju H. Park (), Tae H. Lee, Yajuan Liu and Jun Chen
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Ju H. Park: Yeungnam University, Department of Electrical Engineering
Tae H. Lee: Chonbuk National University, Division of Electronic Engineering
Yajuan Liu: North China Electric Power University, Control and Computer Engineering
Jun Chen: Jiangsu Normal University, School of Electrical Engineering and Automation

Chapter 6 in Dynamic Systems with Time Delays: Stability and Control, 2019, pp 155-176 from Springer

Abstract: Abstract This chapter is devoted to the study of the stability of time-delay neural networks in both continuous and discrete contexts. Augmented Lyapunov–Krasovskii (L–K) functionals are deliberately constructed for both continuous and discrete cases, in which state and delay information of neural networks is fully taken into account. During the process of dealing with the time derivative (or the forward difference) of L–K functionals, the integral (or summation) inequalities are employed to estimate integral (or summation) terms. Consequently, more relaxed conditions are derived in the forms of linear matrix inequalities. Several numerical examples are presented to show the effectiveness of the proposed approach.

Keywords: Integral/summation inequality; Lyapunov–Krasovskii functional; Neural networks; Stability; Time delay (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-9254-2_6

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DOI: 10.1007/978-981-13-9254-2_6

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